Half Pound Filter for Real-Time Animation Blending

TL;DR

This paper presents the Half Pound Filter (HPF), an improved real-time filtering technique for animation blending that enhances fidelity and reduces artifacts during human animation replay, with automatic tuning and trigger mechanisms.

Contribution

The paper introduces the Half Pound Filter (HPF), a novel modification of the 1 Euro Filter, with algorithms for automatic data-driven tuning and motion-based trigger control for real-time animation filtering.

Findings

HPF outperforms traditional filters in animation fidelity.

Automatic tuning improves filter adaptability in real-time.

Higher quality animation replay demonstrated on LaFAN1 dataset.

Abstract

This paper introduces the Half Pound Filter (HPF) as a modification of the 1 Euro Filter (1EF) and algorithms for automatic data-driven tuning and for filter triggering based on motion derivative boundary checks. An application of the filter is presented in the context of human animation replay for real-time simulations, where switches in animation clips cause discontinuities that must be hidden by filtering the bone trajectory without introducing noticeable artifacts. The quality of the filtering will be compared with other common animation filtering techniques using an example case drawn fromthe LaFAN1 dataset, showing that the resulting animation is replayed with higher fidelity by evaluating the Mean Squared Error (MSE) and Normalized Power Spectrum Similarity (NPSS) for each setup. Performances will be evaluated using both a standard predetermined triggerpoint and blending distance and the automatic blending trigger and recovery system.